Recommender systems serve to provide tailored suggestions, guiding users through the vast array of options available online to find items that match their preferences. Information overload, the difficulty of processing and making sense of an excessive quantity of data, is a problem that these solutions successfully alleviate. They achieve this by personalizing and prioritizing the content for users. With the growth of the Internet, recommender systems have evolved to include three main techniques for filtering data: content-based filtering, collaborative filtering, and a combination of both in a hybrid approach. This document offers a detailed overview of recommender systems, exploring the principles behind content-based, collaborative, and hybrid methods for enhancing the user experience by recommending items of interest from a constantly expanding pool of data.